d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation
This work addresses a key efficiency bottleneck for researchers and practitioners using diffusion-based language models, offering a balanced solution for faster text generation.
The paper tackles the accuracy-parallelism trade-off in diffusion large language models by proposing d3LLM, which uses pseudo-trajectory distillation and entropy-based decoding to achieve up to 10x speedup over baseline diffusion models and 5x over autoregressive models with minimal accuracy loss.
Diffusion large language models (dLLMs) offer capabilities beyond those of autoregressive (AR) LLMs, such as parallel decoding and random-order generation. However, realizing these benefits in practice is non-trivial, as dLLMs inherently face an accuracy-parallelism trade-off. Despite increasing interest, existing methods typically focus on only one-side of the coin, targeting either efficiency or performance. To address this limitation, we propose d3LLM (Pseudo-Distilled Diffusion Large Language Model), striking a balance between accuracy and parallelism: (i) during training, we introduce pseudo-trajectory distillation to teach the model which tokens can be decoded confidently at early steps, thereby improving parallelism; (ii) during inference, we employ entropy-based multi-block decoding with a KV-cache refresh mechanism to achieve high parallelism while maintaining accuracy. To better evaluate dLLMs, we also introduce AUP (Accuracy Under Parallelism), a new metric that jointly measures accuracy and parallelism. Experiments demonstrate that our d3LLM achieves up to 10$\times$ speedup over vanilla LLaDA/Dream and 5$\times$ speedup over AR models without much accuracy drop. Our code is available at https://github.com/hao-ai-lab/d3LLM.